fix min/max/median for 2D arrays

Author: tswast

db_dtypes/core.py

@@ -135,29 +135,35 @@ def min(self, *, axis: Optional[int] = None, skipna: bool = True, **kwargs):
         result = pandas_backports.nanmin(
             values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
         )
-        return self._box_func(result)
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
 
     def max(self, *, axis: Optional[int] = None, skipna: bool = True, **kwargs):
         pandas_backports.numpy_validate_max((), kwargs)
         result = pandas_backports.nanmax(
             values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
         )
-        return self._box_func(result)
-
-    if pandas_release >= (1, 2):
-
-        def median(
-            self,
-            *,
-            axis: Optional[int] = None,
-            out=None,
-            overwrite_input: bool = False,
-            keepdims: bool = False,
-            skipna: bool = True,
-        ):
-            pandas_backports.numpy_validate_median(
-                (),
-                {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims},
-            )
-            result = pandas_backports.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
+
+    def median(
+        self,
+        *,
+        axis: Optional[int] = None,
+        out=None,
+        overwrite_input: bool = False,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        if not hasattr(pandas_backports, "numpy_validate_median"):
+            raise NotImplementedError("Need pandas 1.3 or later to calculate median.")
+
+        pandas_backports.numpy_validate_median(
+            (), {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims},
+        )
+        result = pandas_backports.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        if axis is None or self.ndim == 1:
             return self._box_func(result)
+        return self._from_backing_data(result)

tests/unit/test_date.py

@@ -65,3 +65,17 @@ def test_date_parsing(value, expected):
 def test_date_parsing_errors(value, error):
     with pytest.raises(ValueError, match=error):
         pandas.Series([value], dtype="dbdate")
+
+
+# TODO: skip if median not available
+@pytest.mark.parametrize(
+    "values, expected",
+    [
+        (["1970-01-01", "1900-01-01", "2000-01-01"], datetime.date(1970, 1, 1)),
+        ([None, "1900-01-01", None], datetime.date(1900, 1, 1)),
+        (["2222-02-01", "2222-02-03"], datetime.date(2222, 2, 2)),
+    ],
+)
+def test_date_median(values, expected):
+    series = pandas.Series(values, dtype="dbdate")
+    assert series.median() == expected